Determination ARIMA Model for the Discharge Acid Concentration in Pressure Oxidation Autoclave at A Gold Processing Plant
Abstract
During industrial production, many quality parameters of products are measured to evaluate the quality of final products. These investigations are also applied for the machine parameters to control if they work properly. A huge amount of data is collected, recorded, and evaluated for these aims. Time series analyses are often a useful tool to evaluate the industrial data to analyze the products quality parameters or machine performances. The autoregressive integrated moving average (ARIMA) time series models are the most known and used method. One of the most important advantages of ARIMA models is its capability of near future estimation for the monitored process variable. For this aim, current research is carried out at a gold processing plant data. The plant gains the gold by applying the modern pressure oxide leaching in autoclave and cyanide leaching. Since the gold ore characteristics can be changed even, they are mined in the same mining area, monitoring the product quality and machine performances during production stages are needed. In this research, the data set, which was recorded at equal sampling time intervals and obtained in a month, is supplied from a gold processing plant in Turkey. During the gold production, the ground gold ore is subjected to the desulphurization process by pressure oxide (POX) in the autoclave. Discharge acid concentration is critical in this process and it is followed regularly by taking samples in 2 hours’ time intervals in 24 hours. Using discharge acid concentration data set, the ARIMA (1,0,1) time series model was determined to monitor it. Also, near-future values of acid concentration were estimated by the ARIMA (1,0,1) model and compared with the real discharge acid concentrations. It was determined that there was a very good agreement between the estimated values obtained by the ARIMA (1,0,1) model and real values.
References
- Ali, A., Iqbal, C., M., Qamar, S., Akhtar, N., Mahmood, T., Hyder, M. & Jamshed, M. T. (2016). Forecasting of daily gold price by using Box-Jenkins methodology. International Journal of Asian Social Science, 6(11), 614-624.
- Bhappu, B. R. (1990). Hydrometallurgical processing of precious metal ores. Mineral Processing and Extractive Metallurgy Review, 6(4), 67-80.
- Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
- Castagliola, P. & Tsung., F. (2005). Autocorrelated SPC for non-normal situations. Quality and Reliability Engineering Journal, 21, 131-161.
- Davis, R., Dedu, V. K. & Bonye, F. (2014). Modeling and forecasting of gold prices on financial markets. American Journal of Contemporary Research, 4(3), 107-113.
- Guha, B. & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2), 117-121.
- Khan, M. M. A. (2013). Forecasting of gold prices (Box Jenkins approach). International Journal of Emerging Technology and Advanced Engineering, 3(3), 662-670.
- Marsden, J. O., & House, C. I. (2006). The chemistry of gold extraction. In Society for Mining, Metallurgy, and Exploration. Littleton.
- Montgomery, D. C., Jennings, C. L. & Kulahçı, M. (Eds.) (2008). Introduction to time series analysis and forecasting. John Wiley & Sons.
- Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
- Rusanen, L., Aromaa, J. & Forsen, O. E. (2013). Pressure oxidation of pyrite-arsenopyrite refractory gold concentrate. Physicochemical Problems of Mineral Processing, 49, 101-109.
- Sharma, A. M. & Baby, S. (2015). Gold price forecasting in India using ARIMA modelling. GE-International Journal of Management Research, 3(10), 14-33.
- Şen, S. (2007). Evaluation of coal-oil assisted gold flotation as a novel processing method for gold recovery. [Unpublished doctoral dissertation]. Dokuz Eylül Üniversitesi.
- Triphaty, N. (2017). Forecasting gold price with autoregressive integrated average model. International Journal of Economics and Financial Issues. 7(4), 324-329.
- M. E. N. R. (2021, October 18). Altın. Ministry of Energy and Nature Reserves. https://enerji.gov.tr/bilgi-merkezi-tabi-kaynaklaraltin
- Datajobs, (2021, October 19). Time series analysis. https://datajobs.com/data-science-repo/Time-Series-Analysis-Guide.pdf
- Altın Madencileri Derneği, (2021, October 25). Türkiye’nin altın potansiyeli-Rezervi ve Üretimi, http://altinmadencileri.org.tr/turkiyenin-altin-potansiyeli/
- Altın Madencileri Derneği, (2021, October 25). Türkiye’nin Altın üretimi. http://altinmadencileri.org.tr/turkiye-altin-uretimi-2/
- World Gold Council, (2021, October 25). How much gold has been mined? https://www.gold.org/about-gold/gold-supply/gold-mining/how-much-gold/
Abstract
References
- Ali, A., Iqbal, C., M., Qamar, S., Akhtar, N., Mahmood, T., Hyder, M. & Jamshed, M. T. (2016). Forecasting of daily gold price by using Box-Jenkins methodology. International Journal of Asian Social Science, 6(11), 614-624.
- Bhappu, B. R. (1990). Hydrometallurgical processing of precious metal ores. Mineral Processing and Extractive Metallurgy Review, 6(4), 67-80.
- Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
- Castagliola, P. & Tsung., F. (2005). Autocorrelated SPC for non-normal situations. Quality and Reliability Engineering Journal, 21, 131-161.
- Davis, R., Dedu, V. K. & Bonye, F. (2014). Modeling and forecasting of gold prices on financial markets. American Journal of Contemporary Research, 4(3), 107-113.
- Guha, B. & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2), 117-121.
- Khan, M. M. A. (2013). Forecasting of gold prices (Box Jenkins approach). International Journal of Emerging Technology and Advanced Engineering, 3(3), 662-670.
- Marsden, J. O., & House, C. I. (2006). The chemistry of gold extraction. In Society for Mining, Metallurgy, and Exploration. Littleton.
- Montgomery, D. C., Jennings, C. L. & Kulahçı, M. (Eds.) (2008). Introduction to time series analysis and forecasting. John Wiley & Sons.
- Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
- Rusanen, L., Aromaa, J. & Forsen, O. E. (2013). Pressure oxidation of pyrite-arsenopyrite refractory gold concentrate. Physicochemical Problems of Mineral Processing, 49, 101-109.
- Sharma, A. M. & Baby, S. (2015). Gold price forecasting in India using ARIMA modelling. GE-International Journal of Management Research, 3(10), 14-33.
- Şen, S. (2007). Evaluation of coal-oil assisted gold flotation as a novel processing method for gold recovery. [Unpublished doctoral dissertation]. Dokuz Eylül Üniversitesi.
- Triphaty, N. (2017). Forecasting gold price with autoregressive integrated average model. International Journal of Economics and Financial Issues. 7(4), 324-329.
- M. E. N. R. (2021, October 18). Altın. Ministry of Energy and Nature Reserves. https://enerji.gov.tr/bilgi-merkezi-tabi-kaynaklaraltin
- Datajobs, (2021, October 19). Time series analysis. https://datajobs.com/data-science-repo/Time-Series-Analysis-Guide.pdf
- Altın Madencileri Derneği, (2021, October 25). Türkiye’nin altın potansiyeli-Rezervi ve Üretimi, http://altinmadencileri.org.tr/turkiyenin-altin-potansiyeli/
- Altın Madencileri Derneği, (2021, October 25). Türkiye’nin Altın üretimi. http://altinmadencileri.org.tr/turkiye-altin-uretimi-2/
- World Gold Council, (2021, October 25). How much gold has been mined? https://www.gold.org/about-gold/gold-supply/gold-mining/how-much-gold/
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2021 |
| Published in Issue | Year 2021Volume: 16 |
Cite
